Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	19,099
2	New Jersey	17,767
3	Massachusetts	13,767
4	Rhode Island	13,681
5	District of Columbia	12,032
6	Connecticut	11,656
7	Delaware	9,418
8	Illinois	9,164
9	Louisiana	8,369
10	Maryland	8,325
11	Nebraska	6,855
12	Iowa	5,918
13	Pennsylvania	5,804
14	Michigan	5,601
15	South Dakota	5,417
16	Indiana	5,033
17	Virginia	4,850
18	Mississippi	4,829
19	Colorado	4,361
20	Georgia	4,084
21	Minnesota	4,070
22	New Mexico	3,511
23	Alabama	3,371
24	Kansas	3,265
25	North Dakota	3,259
26	New Hampshire	3,227
27	Tennessee	3,186
28	Wisconsin	2,955
29	Ohio	2,901
30	Washington	2,866
31	Utah	2,792
32	Nevada	2,677
33	California	2,633
34	Florida	2,480
35	North Carolina	2,442
36	Arizona	2,440
37	Arkansas	2,166
38	Kentucky	2,128
39	South Carolina	2,095
40	Texas	2,095
41	Missouri	2,088
42	Maine	1,628
43	Oklahoma	1,584
44	Vermont	1,560
45	Idaho	1,550
46	Wyoming	1,513
47	West Virginia	1,108
48	Puerto Rico	1,091
49	Oregon	968
50	Alaska	587
51	Montana	453
52	Hawaii	449

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Nebraska	156
2	Virginia	143
3	Maryland	142
4	Rhode Island	134
5	District of Columbia	126
6	Iowa	110
7	Alabama	104
8	Illinois	102
9	Mississippi	102
10	New Jersey	102
11	Minnesota	96
12	Wisconsin	89
13	Massachusetts	78
14	South Dakota	77
15	Louisiana	71
16	Delaware	70
17	Indiana	69
18	New York	67
19	Connecticut	64
20	Georgia	61
21	Tennessee	60
22	California	59
23	Arkansas	56
24	Arizona	55
25	New Mexico	53
26	North Carolina	51
27	Pennsylvania	50
28	Colorado	49
29	Texas	49
30	New Hampshire	47
31	Kentucky	45
32	Utah	44
33	Ohio	41
34	South Carolina	39
35	Michigan	37
36	West Virginia	37
37	Washington	35
38	Nevada	31
39	Maine	28
40	Missouri	28
41	Kansas	27
42	Florida	23
43	Puerto Rico	23
44	Idaho	19
45	Wyoming	19
46	Oklahoma	15
47	North Dakota	11
48	Oregon	10
49	Alaska	8
50	Vermont	6
51	Montana	1
52	Hawaii	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,513
2	New Jersey	1,283
3	Connecticut	1,073
4	Massachusetts	963
5	District of Columbia	641
6	Rhode Island	639
7	Louisiana	589
8	Michigan	537
9	Pennsylvania	421
10	Illinois	412
11	Maryland	401
12	Delaware	354
13	Indiana	307
14	Colorado	246
15	Mississippi	232
16	Georgia	182
17	Ohio	179
18	Minnesota	173
19	New Hampshire	170
20	Iowa	161
21	New Mexico	159
22	Virginia	156
23	Washington	146
24	Nevada	133
25	Alabama	120
26	Arizona	117
27	Missouri	116
28	Florida	110
29	California	102
30	Kentucky	94
31	Wisconsin	94
32	South Carolina	91
33	Vermont	88
34	Nebraska	87
35	Oklahoma	82
36	North Carolina	81
37	North Dakota	78
38	Kansas	71
39	Maine	62
40	South Dakota	61
41	Texas	55
42	Tennessee	52
43	Idaho	45
44	Arkansas	41
45	Puerto Rico	41
46	West Virginia	41
47	Oregon	35
48	Utah	33
49	Wyoming	25
50	Montana	15
51	Hawaii	12
52	Alaska	10

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Rhode Island	21
2	Massachusetts	10
3	New Jersey	9
4	Illinois	8
5	Connecticut	7
6	District of Columbia	6
7	Maryland	6
8	Mississippi	6
9	Pennsylvania	6
10	Colorado	5
11	Iowa	5
12	Minnesota	5
13	New Hampshire	5
14	New York	5
15	Virginia	5
16	Delaware	4
17	Indiana	4
18	Michigan	4
19	Georgia	3
20	Louisiana	3
21	Ohio	3
22	Arizona	2
23	Nebraska	2
24	New Mexico	2
25	North Carolina	2
26	North Dakota	2
27	Wisconsin	2
28	Alabama	1
29	California	1
30	Florida	1
31	Kentucky	1
32	Maine	1
33	Missouri	1
34	Nevada	1
35	Oklahoma	1
36	South Carolina	1
37	South Dakota	1
38	Tennessee	1
39	Washington	1
40	Wyoming	1
41	Alaska	0
42	Arkansas	0
43	Hawaii	0
44	Idaho	0
45	Kansas	0
46	Montana	0
47	Oregon	0
48	Puerto Rico	0
49	Texas	0
50	Utah	0
51	Vermont	0
52	West Virginia	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	123,449	1	99
Dakota	Nebraska	82,443	2	99
Lincoln	Arkansas	74,862	3	99
Nobles	Minnesota	69,166	4	99
Lake	Tennessee	58,865	5	99
Davidson	Tennessee	7,415	263	91
Richland	South Carolina	3,444	690	78
Pierce	Washington	2,114	1030	67
Orange	California	1,842	1152	63
York	South Carolina	1,320	1461	53

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Terrell	Georgia	3,048	1	99
Early	Georgia	2,944	2	99
Randolph	Georgia	2,803	3	99
Hancock	Georgia	2,601	4	99
Essex	New Jersey	2,038	5	99
Richland	South Carolina	159	580	81
Davidson	Tennessee	86	845	73
Pierce	Washington	86	847	73
Orange	California	45	1197	61
York	South Carolina	25	1443	54

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.